Global trends in both technology and regulation are causing U.S. insurers to reflect more deepely on their model risk management frameworks. The world has seen a rapid proliferation of AI-based tools accompanied with an overall increased usage of machine learning models, exacerbating model risk and reducing transparency in an unprecedented manner. This rising tide of AI means that it is imperative for U.S.-based insurers to address these emerging risks within their Model Risk Management (MRM) frameworks. Regulation also continues to evolve in this space. In Canada, the Office of the Superintendent of Financial Institutions (OSFI) revised Guideline E-23 to instill more detailed model risk management standards. In Bermuda, the Bermuda Monetary Authority (BMA) has published CP2 to add increased scrutiny on the data and models used by insurers. Insurers must react: MRM frameworks require prudent revisions to reflect these ever-changing landscapes of risk. Reassessing the core pillars of MRM frameworks - risk measurement, model development, validation, monitoring, and governance - is key to understanding the consequences of these evolving market changes on insurers' risk profile. Overall, the inclusion of these new tools into the world of insurance necessitates a thorough examination of insurers' MRM frameworks in order to align these new risks with core MRM principles. Learning objectives: In this session we will: - Reflect on recent drivers of change and how they should be considered in the context of MRM - Explore best practices as it relates to MRM frameworks in the context of current global trends - Describe challenges, success stories, and lessons learned relating to recalibrating and embedding MRM frameworks